Using Big Data Analytics and Statistical Methods for Improving Drug Safety

This dissertation includes three studies, all focusing on utilizing Big Data and statistical methods for improving one of the most important aspects of health care, namely drug safety. In these studies we develop data analytics methodologies to inspect, clean, and model data with the aim of fulfilling the three main goals of drug safety; detection, understanding, and prediction of adverse drug effects.In the first study, we develop a methodology by combining both analytics and statistical methods with the aim of detecting associations between drugs and adverse events through historical patients' records. Particularly we show applicability of the developed methodology by focusing on investigating potential confounding role of common diabetes drugs on developing acute renal failure in diabetic patients. While traditional methods of signal detection mostly consider one drug and one adverse event at a time for investigation, our proposed methodology takes into account the effect of drug-drug interactions by identifying groups of drugs frequently prescribed together.In the second study, two independent methodologies are developed to investigate the role of prescription sequence factor on the likelihood of developing adverse events. In fact, this study focuses on using data analytics for understanding drug-event associations. Our analyses on the historical medication records of a group of diabetic patients using the proposed approaches revealed that the sequence in which the drugs are prescribed, and administered, significantly do matter in the development of adverse events associated with those drugs.The third study uses a chronological approach to develop a network of approved drugs and their known adverse events. It then utilizes a set of network metrics, both similarity- and centrality-based, to build and train machine learning predictive models and predict the likely adverse events for the newly discovered drugs before their approval and introduction to the market. For this purpose, data of known drug-event associations from a large biomedical publication database (i.e., PubMed) is employed to construct the network. The results indicate significant improvements in terms of accuracy of prediction of drug-evet associations compared with similar approaches

No place like home: cross-national data analysis of the efficacy of social distancing during the COVID-19 pandemic

Background: In the absence of a cure in the time of a pandemic, social distancing measures seem to be the most effective intervention to slow the spread of disease. Various simulation-based studies have been conducted to investigate the effectiveness of these measures. While those studies unanimously confirm the mitigating effect of social distancing on disease spread, the reported effectiveness varies from 10% to more than 90% reduction in the number of infections. This level of uncertainty is mostly due to the complex dynamics of epidemics and their time-variant parameters. However, real transactional data can reduce uncertainty and provide a less noisy picture of the effectiveness of social distancing. Objective: The aim of this paper was to integrate multiple transactional data sets (GPS mobility data from Google and Apple as well as disease statistics from the European Centre for Disease Prevention and Control) to study the role of social distancing policies in 26 countries and analyze the transmission rate of the coronavirus disease (COVID-19) pandemic over the course of 5 weeks. Methods: Relying on the susceptible-infected-recovered (SIR) model and official COVID-19 reports, we first calculated the weekly transmission rate (β) of COVID-19 in 26 countries for 5 consecutive weeks. Then, we integrated these data with the Google and Apple mobility data sets for the same time frame and used a machine learning approach to investigate the relationship between the mobility factors and β values. Results: Gradient boosted trees regression analysis showed that changes in mobility patterns resulting from social distancing policies explain approximately 47% of the variation in the disease transmission rates. Conclusions: Consistent with simulation-based studies, real cross-national transactional data confirms the effectiveness of social distancing interventions in slowing the spread of COVID-19. In addition to providing less noisy and more generalizable support for the idea of social distancing, we provide specific insights for public health policy makers regarding locations that should be given higher priority for enforcing social distancing measures

Deep Learning with Minimal Coding Effort: A Tutorial on Theory and Implementation of Deep Artificial Neural Networks

Advances in computer technologies in the past couple of decades has enabled data and computer scientists to employ deep neural networks to detect and analyze complex patterns in large and varied data repositories from a wide variety of application domains. Given the interest in big data and analytics coursework in most information systems departments, this paper provides a step-by-step tutorial on the design and implementation of deep neural networks using an open-source, low-code, intuitive analytics platform. This platform (KNIME) suits well for both technical and non-technical users. Although this tutorial focuses on an image processing (classification) project in the popular context of healthcare, we believe the provided guidelines, with slight modifications, can be applied to the design and implementation of various deep learning architectures built to analyze a wide variety of data types

Adjusting COVID-19 reports for countries' age disparities: a comparative framework for reporting performances

Objectives: The COVID-19 outbreak has impacted distinct health care systems differently. While the rate of disease for COVID-19 is highly age-variant, there is no unified and age/gender-inclusive reporting taking place. This renders the comparison of individual countries based on their corresponding metrics, such as CFR difficult. In this paper, we examine cross-country differences, in terms of the age distribution of symptomatic cases, hospitalizations, intensive care unit (ICU) cases, and fatalities. In addition, we propose a new quality measure (called dissonance ratio) to facilitate comparison of countries’ performance in testing and reporting COVID-19 cases (i.e., their reporting quality). Methods: By combining population pyramids with estimated COVID-19 age dependent conditional probabilities, we bridge country-level incidence data gathered from different countries and attribute the variability in data to country demographics. Results: We show that age-adjustment can account for as much as a 22-fold difference in the expected number of fatalities across different countries. We provide case, hospitalization, ICU, and fatality breakdown estimates for a comprehensive list of countries. Also, a comparison is conducted between countries in terms of their performance in reporting COVID-19 cases and fatalities. Conclusions: Our research sheds light on the importance of and propose a methodology to use countries’ population pyramids for obtaining accurate estimates of the healthcare system requirements based on the experience of other, already affected, countries at the time of pandemics

An explanatory machine learning framework for studying pandemics: The case of COVID-19 emergency department readmissions

ArticleInPressOne of the major challenges that confront medical experts during a pandemic is the time required to identify and validate the risk factors of the novel disease and to develop an effective treatment protocol. Traditionally, this process involves numerous clinical trials that may take up to several years, during which strict preventive measures must be in place to control the outbreak and reduce the deaths. Advanced data analytics techniques, however, can be leveraged to guide and speed up this process. In this study, we combine evolutionary search algorithms, deep learning, and advanced model interpretation methods to develop a holistic exploratory- predictive-explanatory machine learning framework that can assist clinical decision-makers in reacting to the challenges of a pandemic in a timely manner. The proposed framework is showcased in studying emergency department (ED) readmissions of COVID-19 patients using ED visits from a real-world electronic health records database. After an exploratory feature selection phase using genetic algorithm, we develop and train a deep artificial neural network to predict early (i.e., 7-day) readmissions (AUC = 0.883). Lastly, a SHAP model is formulated to estimate additive Shapley values (i.e., importance scores) of the features and to interpret the magnitude and direction of their effects. The findings are mostly in line with those reported by lengthy and expensive clinical trial studies

Training, Self-Efficacy, and Performance; a Replication Study

A conceptual replication of multiple prior IS studies was conducted with the aim of providing stronger empirical support for those results. Conducting six separate longitudinal studies, the effect of professional training on improving one’s application-specific computer self-efficacy (AS-CSE) was shown. Also in line with some prior IS studies it was shown that an application-specific measure of self-efficacy is better able to predict one’s performance in accomplishing tasks in the corresponding domain than a general computer self-efficacy (GCSE) measure. Moreover, it is shown that, regardless of the type and characteristics of the training method, individuals’ perceptions of quality of training significantly affects their AS-CSE after the training course

An explanatory analytics framework for early detection of chronic risk factors in pandemics

Timely decision-making in national and global health emergencies such as pandemics is critically important from various aspects. Especially, early identification of risk factors of contagious viral diseases can lead to efficient management of limited healthcare resources and saving lives by prioritizing at-risk patients. In this study, we propose a hybrid artificial intelligence (AI) framework to identify major chronic risk factors of novel, contagious diseases as early as possible at the time of pandemics. The proposed framework combines evolutionary search algorithms with machine learning and the novel explanatory AI (XAI) methods to detect the most critical risk factors, use them to predict patients at high risk of mortality, and analyze the risk factors at the individual level for each high-risk patient. The proposed framework was validated using data from a repository of electronic health records of early COVID-19 patients in the US. A chronological analysis of the chronic risk factors identified using our proposed approach revealed that those factors could have been identified months before they were determined by clinical studies and/or announced by the United States health officials.2-s2.0-851231973383506862

The Role of Parallelism in Resolving the Privacy Paradox of Information Disclosure in Social Networks

In this study, by developing a new conceptual model and empirically testing it using a scenario-based survey conducted on a group of 180 Facebook users, we address the privacy paradox characterized in the literature as the inconsistency between individua